This invention relates to apparatus, systems and methods for the detection of abnormal time intervals between events, in particular within the context of the remote monitoring of the wellbeing of persons.
“Telecare” is a term describing the use of technology to enable parties such as care providers to monitor the status of persons who may be elderly or otherwise vulnerable (referred herein as customers), where such customers remain in their own homes or are otherwise located remote to the care providers.
Various telecare approaches are known. In the most direct method of obtaining customer data, customers wear devices which measure certain physical or physiological parameters. For example, an accelerometer-based equipment worn on the person can provide direct feedback to the care provider that the customer has fallen. However this method suffers from being excessively invasive to the customer who may have to have on his person a number of such devices. Compliance is also a problem where the customer is reluctant to cooperate by wearing the device(s) especially if interaction with a complex user interface is required.
A preferred approach is to provide sensors fixed within the customer's premises, to monitor the customer's activities. This can be achieved by use of devices to literally keep an eye on the customer, such as video cameras and sound recording devices. However this approach could also be ethically objectionable and invasive; it is also expensive and complicated to set up and monitor.
A third and still less invasive approach is to capture data indirectly about the customer's movements and actions. Such data is obtained by use of ambient sensors such as passive infrared (PIR) motion sensors, sensors to detect door and window closure, meters to detect use of water, electric and gas, and the like. These devices can continually capture information about the activity and inactivity levels and patterns of the customer with greater subtlety than by use of wearable devices or by video cameras. As being technologically and commercially mature technologies, such sensors are relatively reliable and inexpensive to obtain, install (especially if they are wireless) and use. Sensed atypical inactivity, in particular, can be mapped to and signify an abnormal event (e.g. when the customer's behaviour deviates from the normal behaviour levels or pattern), such as a fall. The sensed data can then be fed into a monitoring system which analyses the data to determine the likelihood of an abnormal event existing, whereupon an alarm can be raised to the remote care providers. In this description, the term “event”, depending on context, includes the occurrence of an event (sometimes “positive event”) as well as the time interval between positive events, or the absence of events (a “negative event” or “non-event”).
The present invention has application in any telecare system using any approach i.e. regardless of how the data is obtained, although the description herein will in the main refer to an embodiment and application in the context of system based on the use of motion sensors, door and window opening and closing sensors, bed occupancy, toilet usage, utility use meters and the like.
In such a system, the gathered information is analysed to determine if a pre-determined condition is met. As an example, periods of non-movement can be identified by motion sensors so that a period of non-movement exceeding a pre-determined length of time is deemed to be unusual, and indicative that the customer has fallen.
In the present description, an “inter-event” time period is the length of time elapsed between consecutive positive events detected by either the same sensor or all sensors within the dwelling of a particular customer. Put another way, an “inter-event” period is the duration of a negative event. In a telecare system, a threshold value defines the boundary of an acceptable inter-event period. If the sensor, or group of sensors, fails to detect an event beyond a set threshold value, this may be deemed to be an abnormal occurrence deserving attention. Thus, the term “inter-event time interval” could also in an appropriate context refer to a time interval following the end of a positive event where there may be a late or even no following positive event, especially in the context of discovering if any positive event ending this time period occurs soon enough to be acceptable.
The main problem for the telecare system operator is in deciding where to set the threshold value of the non-movement period. If the value is set too low, then an excessive number of false alarms will be generated, annoying all concerned and more significantly, reducing trust in the system. Setting the threshold level too high however, carries the risk of an alarm being raised late which means that assistance would be sent late to the customer needing help.
There are currently two main approaches to the determination of the threshold value. In the first, a fixed value is provided at the outset. This is currently the prevalent method deployed by telecare operators, where sensors (also here referred to as nodes) have their threshold values either factory-set, or set by the parties installing the sensors at the premises or dwellings. The threshold value is thus often at best an educated guess about its applicability or accuracy in the particular implementation. Even where the value can be subsequently changed, this is a clumsy method requiring much separate measurement and monitoring before the value can be manually re-set.
“Case Studies from the Liverpool Telecare Pilot” Barnes N, Webster S, Mizutani T, Reeves A, Ng J, Buckland M describes an adaptive approach which can be personalised to the customer through the system's “learning” the customer's behaviour and habits over time. The learning process is based on data gathered by a system of individual sensors or nodes within the customer's home and a gateway device to which all the nodes are connected. The threshold levels initially used are either factory-set, or else are set by the installers. As the sensor nodes gather data over time, it or the gateway device builds a statistical profile of the normal activity levels and behavioural patterns of the particular customer and uses this profile as the basis for predicting future behaviour. A problem arises however with the setting of the threshold value in this scenario. The applicant's experience in this field is that it can take weeks (in an optimistic scenario) or even months for accurate and reliable threshold values to be established. Indeed, in some cases it has been found that a reliable threshold value cannot be set even after 2.5 years. Until then, the system is still “learning” and while capable of sensing positive events, there is no threshold value to refer to. Unless and until a threshold value is sent, it is incapable of full operation to sense the occurrence of an abnormal event so as to generate alerts in a trusted manner in the event of customer difficulties. The length of time needed for a telecare system to complete the “learning” or “training” phase and to establish threshold values is a problem and a significant barrier to the commercialisation and mass take-up of telecare systems.
There is thus a need for a system capable of becoming fully operational by establishing and setting threshold values within a shorter space of time. The present invention seeks to address this issue.
In a first aspect of the invention, there is provided a system for determining if a time period after a sensing node has sensed an event exceeds a threshold value, including                establishing means for establishing a plurality of reference threshold values, wherein each reference threshold value is associated with a set of reference inter-event time intervals or metrics statistically derived therefrom,        calculating means for calculating a set of preliminary time intervals or metrics statistically derived therefrom, based on events sensed by the sensing node,        comparing means for comparing the set of preliminary inter-event time intervals or metrics statistically derived therefrom, with each set of reference inter-event time intervals or metrics statistically derived therefrom,        identifying means for identifying the reference threshold value associated with the set of reference inter-event time intervals or metrics statistically derived therefrom, being the closest match to the set of preliminary inter-event time intervals or metrics statistically derived therefrom, and        determining means for determining if, upon the sensing node sensing a further event, the time period after the sensing node has sensed the event exceeds the identified reference threshold value before a yet further event is sensed by the sensing node or an associated sensing node.        
In the invention, a sensor or a node which is newly-installed in a dwelling can refer to a threshold value which has already been established and stored for the use of the new node. In this way, the “learning period” of the new node to establish its own threshold value is either eliminated or considerably reduced. The threshold level represents the boundary of a normal time interval between sensed events—failure to sense an event within the normal time period or interval has expired indicates an abnormal event to be investigated and may be cause to raise an alarm.
The stored threshold value can be one which has been pre-determined e.g. by the system administrator from experience or historical data, or one which is established by an “established” node already in use. In a preferred embodiment however, there are a number of stored threshold values generated by a number of established nodes. In this case, the new node can select from the number of reference threshold values, where each threshold value is associated with either a particular node, or cluster of nodes, or more preferably, with a set of metrics statistically derived from data about events sensed by the established or reference nodes, each metric in the set being such as to have caused a respective threshold value, equal or close to the respective reference value, to have been selected either manually or automatically for the respective established or reference node. The new node selects the node by first sensing a few events of its own after installation. These sensed events are compared with the sensed data obtained by the established node(s) for a match. Alternatively and more preferably, instead of comparing raw data with raw data, the raw data sensed by the nodes is processed using statistical methods (e.g. Standard Deviation) to produce metrics which are then used for comparison purposes.
The established node whose sensed data (which reference shall, where appropriate include metrics statistically derived from the sensed data), most closely matches the new node's own sensed data is selected, and the threshold value associated with that established node set for the new node.
It can be seen that the node types need not be the same for the application of this invention, i.e. if sensed data from a PIR sensor matches the sensed data from a gas metering device (e.g. there is a sensed event indicating activity or use every two hours)—the threshold value for the PIR sensor could well be applied for the gas meter. However it may be expected that sensed data between nodes sensing similar types of data are more likely to produce threshold values which are useful to newly-established which are similarly set up to sense similar types of data.
The sensed data is preferably described in statistical terms, especially by reference to the mean and standard deviation of the gathered data. The matching method can be carried out using any conventional technique: if the data is described statistically then it is sensible to use statistical methods to analyse the data to find a match e.g. by using a case based reasoning engine.
In a further preferred embodiment, a number of threshold values are established for the node. These threshold values could correspond to time of day periods where each period comprises e.g. two hours. For example, it may be expected that a sensor node in the kitchen may more frequently detect activity during meal times than during the midnight hours. Thus different threshold levels will be set for the hours of 18:00 to 20:00, as compared to 02:00 to 04:00.
In a second aspect of the invention, there is provided a system according to the claimed invention operable to monitor a telecare customer within a customer dwelling comprising                a plurality of associated sensing nodes each of which is operable to sense events associated with the customer's activities within the customer dwelling, and        a gateway device connected to each of the associated sensing nodes for receiving sensed data therefrom, the gateway device incorporating the determining means.        
Preferably, the reference threshold values are established by nodes which have already gone through a full learning or training period and which already have associated with them established threshold values for reference by the newly-installed node. Such threshold values may alternatively be obtained from yet even-earlier nodes, and so on.
In a third aspect of the invention, there is provided a gateway device for use in a system according to the second aspect of the invention which is operable in use, to receive sensed data from a plurality of associated sensing nodes and to receive identified reference threshold values from a remote identifying means, and incorporating determining means for determining if, upon one of the sensing nodes sensing an event, the time period after the sensing node has sensed the event exceeds a respective threshold value before a further event is sensed by one of the said sensing nodes.
The sensing nodes can be configured to communicate directly with a database which holds threshold values already established by the established nodes, to enable the newly-installed node to obtain the threshold value most closely corresponding to the data sensed by the new node during its learning phase. More preferably however, a gateway device, which communicates with the new sensing node and the database (which can be located locally or remotely to the new node) obtains the appropriate threshold value for the new node. The gateway device holds this threshold value for the particular new node, and upon the new node sensing a further event, the sensed data of the further event is sent to the gateway which performs a comparison to determine of the time period elapsing after the sensed event exceeds the threshold obtained from the database. The gateway can be configured for some or all the sensing nodes in the dwelling or premises of the customer, so that the threshold appropriate to each node is obtained from the database and held by the gateway device for the node concerned. This further allows for the gateway to collate the data sensed by each node which it is connected to, and to provide a more holistic view of the activity levels and behavioural patterns by the customer within the dwelling.
In a fourth aspect of the invention, there is provided a method for determining if a time period after a sensing node has sensed an event exceeds a threshold inter-event time interval, including the steps of                establishing a plurality of reference threshold values, wherein each reference threshold value is associated with a set of reference inter-event time intervals or metrics statistically derived therefrom,        calculating a set of preliminary inter-event time intervals based on the sensing of a plurality of events by the sensing node,        comparing the set of preliminary inter-event time intervals or metrics statistically derived therefrom, with each set of reference inter-event time intervals or metrics statistically derived therefrom,        identifying the reference threshold value associated with the set of reference inter-event time intervals or metrics statistically derived therefrom being the closest match to the set of preliminary inter-event time intervals or metrics statistically derived therefrom, and        upon the sensing node sensing a further event, determining if the time period after the sensing node has sensed the further event exceeds the identified reference threshold value before a yet further event is sensed either by the sensing node or an associated sensing node.        
In the invention, a node can be newly-installed but still be capable of functioning to detect abnormal time intervals indicating a problem, either immediately upon installation or relatively quickly, by referring to a threshold value it need not establish for itself.
A newly-installed node can also be set up to go through a learning period wherein all data gathered from the events it detects go solely or mainly to setting a threshold value for its own use. As mentioned above, establishing a threshold value in this way will take a longer time than the method wherein the new node uses a value obtained from an established node which exhibits the same or similar event detection pattern as the new node.
In the invention, the method of detecting an excessively long time interval between sensed events indicating a possible cause for concern, is carried out by the new detection node by reference to a threshold value which it has not learned for itself.
The threshold value can be dynamically and automatically re-established or refreshed as the node continues detecting events. This allows for the threshold value, initially obtained from an established node, to be honed to greater accuracy. In one embodiment, such improvements could be communicated back to update the threshold values associated with the established nodes.
In the event that no reference to threshold values by established nodes is possible, the initial establishment of a threshold value may take longer. However an adaptive approach to dynamically set threshold values allows for greater customisation and personalisation to the particular function of the node, its location and the activities of the specific telecare customer. In this way, use of an adaptive method is an improvement on current methods where a static threshold value is manually set (and re-set) for nodes. Furthermore it is useful in the context where a network of nodes may not be available e.g. where there is lack of network connectivity, or where only the one node is needed.